# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm
# DeiT: https://github.com/facebookresearch/deit
# --------------------------------------------------------

from functools import partial

import torch
import torch.nn as nn

from timm.models.vision_transformer import PatchEmbed, Block

from util.pos_embed import get_2d_sincos_pos_embed


class MaskedAutoencoderViT(nn.Module):
    """
    使用VisionTransformer（ViT）作为骨干的Masked Autoencoder（MAE）模型
    """
    def __init__(self, img_size=224, patch_size=16, in_chans=3,
                 embed_dim=1024, depth=24, num_heads=16,
                 decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
                 mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False):
        super().__init__()

        # --------------------------------------------------------------------------
        # 编码器部分
        # 使用PatchEmbed将输入图像划分为图像块，并通过线性映射将每个图像块映射到嵌入向量空间。
        # 通过一个可学习的位置嵌入（pos_embed）和一个表示类别的可学习嵌入（cls_token）来增强输入嵌入向量。
        # 通过多个Transformer块（Block）进行特征的编码。
        # 使用LayerNormalization进行归一化。

        self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim)
        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))  # 分类token
        # 固定的sin-cos位置嵌入
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False)

        # 定义Transformer的多个Block
        self.blocks = nn.ModuleList([
            Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)
            for i in range(depth)
        ])
        self.norm = norm_layer(embed_dim)  # LayerNorm归一化层
        # --------------------------------------------------------------------------

        # --------------------------------------------------------------------------
        # 解码器部分
        # 使用一个线性层（decoder_embed）将编码的特征映射到较低维度的空间， 即解码器的嵌入维度（decoder_embed_dim）。
        # 引入一个表示遮蔽的可学习嵌入（mask_token）。
        # 通过位置嵌入（decoder_pos_embed）增强解码器的输入。
        # 通过多个Transformer块（decoder_blocks）进行特征的解码。
        # 使用线性层（decoder_pred）将解码的特征映射回图像块的原始空间。

        self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True)

        self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))  # 遮蔽token

        # 固定的sin-cos位置嵌入
        self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim), requires_grad=False)

        self.decoder_blocks = nn.ModuleList([
            Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)
            for i in range(decoder_depth)
        ])
        self.decoder_norm = norm_layer(decoder_embed_dim)  # 解码器的LayerNorm
        self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True)  # 从解码器到patch
        # --------------------------------------------------------------------------

        self.norm_pix_loss = norm_pix_loss  # 是否使用像素级的归一化损失

        self.initialize_weights()  # 初始化权重

    def initialize_weights(self):
        # 初始化权重
        # 通过sin-cos嵌入初始化（并冻结）位置嵌入
        pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True)
        self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))

        decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True)
        self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0))

        # 初始化patch_embed的权重
        w = self.patch_embed.proj.weight.data
        torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))

        # 使用正态分布初始化分类token和遮蔽token
        torch.nn.init.normal_(self.cls_token, std=.02)
        torch.nn.init.normal_(self.mask_token, std=.02)

        # 初始化Linear层和LayerNorm层
        self.apply(self._init_weights)

    def _init_weights(self, m):
        # 为Linear层和LayerNorm层初始化权重
        if isinstance(m, nn.Linear):
            torch.nn.init.xavier_uniform_(m.weight)  # 使用Xavier初始化
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    def patchify(self, imgs):
        """
        将输入的图像分割成图像块
        imgs: (N, 3, H, W)
        返回：x: (N, L, patch_size**2 * 3)，每个图像块的特征表示
        """
        p = self.patch_embed.patch_size[0]
        assert imgs.shape[2] == imgs.shape[3] and imgs.shape[2] % p == 0

        h = w = imgs.shape[2] // p
        x = imgs.reshape(shape=(imgs.shape[0], 3, h, p, w, p))
        x = torch.einsum('nchpwq->nhwpqc', x)  # 重排维度
        x = x.reshape(shape=(imgs.shape[0], h * w, p**2 * 3))
        return x

    def unpatchify(self, x):
        """
        将图像块恢复成原始图像
        x: (N, L, patch_size**2 * 3)
        返回：imgs: (N, 3, H, W)，恢复后的图像
        """
        p = self.patch_embed.patch_size[0]
        h = w = int(x.shape[1]**.5)
        assert h * w == x.shape[1]

        x = x.reshape(shape=(x.shape[0], h, w, p, p, 3))
        x = torch.einsum('nhwpqc->nchpwq', x)
        imgs = x.reshape(shape=(x.shape[0], 3, h * p, h * p))
        return imgs

    def random_masking(self, x, mask_ratio):
        """
        执行每个样本的随机遮蔽
        x: 输入序列 [N, L, D]，每个图像块的特征
        mask_ratio: 遮蔽比例
        返回：x_masked: 被遮蔽后的特征，mask: 二进制遮蔽掩码，ids_restore: 用于恢复原顺序的索引
        """
        N, L, D = x.shape
        len_keep = int(L * (1 - mask_ratio))

        noise = torch.rand(N, L, device=x.device)  # 在[0, 1]之间生成随机噪声

        ids_shuffle = torch.argsort(noise, dim=1)  # 按照噪声升序排列，较小的噪声保留，较大的噪声被遮蔽
        ids_restore = torch.argsort(ids_shuffle, dim=1)  # 恢复索引

        ids_keep = ids_shuffle[:, :len_keep]  # 保留前len_keep个元素
        x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))  # 获取被保留的特征

        # 生成二进制掩码，0表示保留，1表示遮蔽
        mask = torch.ones([N, L], device=x.device)
        mask[:, :len_keep] = 0
        mask = torch.gather(mask, dim=1, index=ids_restore)

        return x_masked, mask, ids_restore

    def forward_encoder(self, x, mask_ratio):
        """
        执行编码器的前向传播
        x: 输入图像的特征
        mask_ratio: 遮蔽比例
        返回：x: 编码后的特征，mask: 二进制遮蔽掩码，ids_restore: 恢复顺序的索引
        """
        x = self.patch_embed(x)  # 对图像进行patching，得到每个patch的特征

        x = x + self.pos_embed[:, 1:, :]  # 添加位置嵌入

        x, mask, ids_restore = self.random_masking(x, mask_ratio)  # 对部分patch进行遮蔽

        cls_token = self.cls_token + self.pos_embed[:, :1, :]  # 添加分类token
        cls_tokens = cls_token.expand(x.shape[0], -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)  # 将分类token和图像特征拼接

        # 通过多个Transformer Block进行编码
        for blk in self.blocks:
            x = blk(x)
        x = self.norm(x)  # 最后进行LayerNorm归一化

        return x, mask, ids_restore

    def forward_decoder(self, x, ids_restore):
        """
        执行解码器的前向传播
        x: 编码后的特征
        ids_restore: 恢复顺序的索引
        返回：x: 解码后的预测图像块
        """
        x = self.decoder_embed(x)  # 解码器嵌入

        # 添加遮蔽token
        mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1)
        x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1)  # 不包括分类token
        x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]))  # 恢复顺序
        x = torch.cat([x[:, :1, :], x_], dim=1)  # 重新拼接分类token

        x = x + self.decoder_pos_embed  # 添加位置嵌入

        # 通过多个Transformer Block进行解码
        for blk in self.decoder_blocks:
            x = blk(x)
        x = self.decoder_norm(x)  # 最后进行LayerNorm归一化

        x = self.decoder_pred(x)  # 通过预测层得到最终的图像块

        # 移除分类token
        x = x[:, 1:, :]

        return x

    def forward_loss(self, imgs, pred, mask):
        """
        计算前向传播的损失
        imgs: 原始图像
        pred: 解码器预测的图像块
        mask: 二进制遮蔽掩码，0表示保留，1表示遮蔽
        """
        target = self.patchify(imgs)  # 将原图像分割成patch

        if self.norm_pix_loss:  # 如果使用像素级归一化
            mean = target.mean(dim=-1, keepdim=True)
            var = target.var(dim=-1, keepdim=True)
            target = (target - mean) / (var + 1.e-6)**.5  # 标准化

        loss = (pred - target) ** 2  # 计算预测与目标之间的平方差
        loss = loss.mean(dim=-1)  # 求每个patch的平均损失

        loss = (loss * mask).sum() / mask.sum()  # 对被遮蔽的patch进行加权求和
        return loss

    def forward(self, imgs, mask_ratio=0.75):
        """
        执行整个MAE的前向传播，包括编码器、解码器和损失计算
        """
        latent, mask, ids_restore = self.forward_encoder(imgs, mask_ratio)  # 执行编码器部分
        pred = self.forward_decoder(latent, ids_restore)  # 执行解码器部分
        loss = self.forward_loss(imgs, pred, mask)  # 计算损失
        return loss, pred, mask


# 定义不同大小的MAE模型
def mae_vit_base_patch16_dec512d8b(**kwargs):
    model = MaskedAutoencoderViT(
        patch_size=16, embed_dim=768, depth=12, num_heads=12,
        decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def mae_vit_large_patch16_dec512d8b(**kwargs):
    model = MaskedAutoencoderViT(
        patch_size=16, embed_dim=1024, depth=24, num_heads=16,
        decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def mae_vit_huge_patch14_dec512d8b(**kwargs):
    model = MaskedAutoencoderViT(
        patch_size=14, embed_dim=1280, depth=32, num_heads=16,
        decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


# 设置推荐的模型结构
mae_vit_base_patch16 = mae_vit_base_patch16_dec512d8b  # 解码器：512维，8层块
mae_vit_large_patch16 = mae_vit_large_patch16_dec512d8b  # 解码器：512维，8层块
mae_vit_huge_patch14 = mae_vit_huge_patch14_dec512d8b  # 解码器：512维，8层块

